Modeling and optimization of an air-cooled photovoltaic thermal (PV/T) system using genetic algorithms

The aim of this paper is to model and optimize an air PV/T system. There are some parameters that affect on the efficiency of a PV/T solar system like thickness of the glass and Tedlar, temperature of the inlet flow, solar cell temperature and etc. All equations for PV cell and thermal collector have been derived. By genetic algorithms using, thermal efficiency and electrical efficiency of the system may be optimized. All the parameters that are used in genetic algorithms, are the parameters that could be changed, and the non-changeable parameters, like solar radiation cannot be used in the algorithm. By compare to other methods, we found that the GaAs are very efficient technique to estimate the design parameters of PV/T solar systems.     

Hydrothermal Characteristics of Spinel Manganese Ferrite Nanofluid in a Metal Foam Tube: Modeling of Experimental Results using Artificial Neural Network

In this research, an experimental evaluation is conducted on the hydrothermal behavior of water-based manganese ferrite nanofluid flowing in a metal foam tube. For this purpose, manganese ferrite nanoparticles are synthesized, and X-ray diffraction and scanning electron microscopy are implemented to specify the samples for determination of phase and size of nanoparticles. The effects of Reynolds number, Prandtl number, and presence of MnFe₂O₄ nanoparticles inside the water on the Nusselt number and friction factor have been studied. The experimental analysis shows that the increment of Reynolds number, Prandtl number, and nanoparticles concentration improve the heat transfer performance. The maximum of 19.1% and 10.5% increase in Nusselt number and friction factor have been achieved respectively by dispersion of 2 wt% manganese ferrite nanoparticles inside the deionized water at Reynolds number of 1,000. A hydrothermal index is proposed to consider the thermal and hydrodynamic characteristics of the nanofluid, and it is attained that the convection heat transfer improvement dominates the pressure drop in this work. According to the experimental results, the Nusselt number and friction factor of the nanofluid is modeled as a function of Reynolds number, Prandtl number, and nanoparticles concentration using artificial neural network with an acceptable precision.     

Inelastic local buckling of curved plates with or without thickness-tapered sections using finite strip method

This paper addresses the inelastic local buckling of the curved plates using finite strip method in which buckling modes and displacements of the curved plate are calculated using sinusoidal shape functions in the longitudinal direction and polynomial functions in the transverse direction. A virtual work formulation is employed to establish the stiffness and stability matrices of the curved plate whilst the governing equations are then solved using a matrix eigenvalue problem. The accuracy and efficiency of the proposed finite strip model is verified with finite element model using ABAQUS as well as the results reported elsewhere while a good agreement is achieved. In order to illustrate the proposed model, a comprehensive parametric study is performed on the steel and aluminium curved plates in which the effects of curvature, the length of the curved plate as well as circumferential boundary conditions on the critical buckling stress are investigated. The developed finite strip method is also used to determine the buckling loads of the curved plates with thickness-tapered sections as well as critical stresses of the aluminium cylindrical sectors that are subjected to uniform longitudinal stresses.     

Enhancing and multi-objective optimising of the performance of Stirling engine using third-order thermodynamic analysis

Stirling engine is an external combustion engine which uses eternal heat sources like solar radiation for heating a compressible fluid inside cylinders. In the recent years, significant attention is drawn to Stirling engines due to the clear advantages, high efficiency potential, flexible fuel, lower nitrogen oxides, quiet and minimal vibration, high reliability and highest specific output work for any closed regenerative cycle. The third order thermal analysis is one of the analyses which has been applied in several studies which have been carried out on Stirling engines. NSGA-II algorithm is applied to optimise the differential regenerator pressure (bar) and the power output (kW) for a Stirling engine system. In this study, three decision-making techniques are utilised to optimise the solutions, obtained of the results. At last, the employed techniques are compared with the data of an experimental research work.     

Polymerization of propylene using MgCl2 (ethoxide type)/TiCl4/diether heterogeneous Ziegler–Natta catalyst

Heterogeneous Ziegler–Natta catalyst of MgCl₂ (ethoxide type)/TiCl₄/diether was prepared. 2,2‐Diisobutyl‐1,3‐dimethoxy propane (DiBDMP), diether, was used as internal donor. Slurry polymerization of propylene was carried out using the catalyst in dry heptane while triethylaluminium (TEA) was used as co‐catalyst. The co‐catalyst effects, such as catalyst molar ratio, polymerization temperature, H₂ pressure, external donor, triisobutylaluminium (TiBA) and monomer pressure, on the activity of the catalyst and isotacticity index (II) of the polymers obtained were studied. Rate of polymerization versus polymerization time is of a decay type with no acceleration period. There are an optimum Al/Ti molar ratio and temperature to obtain the highest activity of the catalyst. The maximum activity was obtained at 60 °C. Increasing the monomer pressure to 1 010 000 Pa linearly increased the activity of the catalyst. Addition of hydrogen to 151 500 Pa pressure increased activity of the catalyst from 2.25 to 5.45 kg polypropylene (PP) (g cat)^?1 h^?1 using 505 000 Pa pressure of monomer. The II decreased with increasing Al/Ti ratio, monomer pressure, hydrogen pressure and increased with increasing temperature to 60 °C, following with decrease as the temperature increases. Productivity of 11.55 kg (PP) (g cat)^?1 h^?1 was obtained at 1 010 000 Pa pressure of monomer and temperature of 60 °C. Addition of methyl p‐toluate (MPT) and dimethoxymethyl cyclohexyl silane (DMMCHS) as external donors decreased the activity of the catalyst sharply, while the II slightly increased. Some studies of the catalyst structure and morphology of the polymer were carried out using FTIR, X‐ray fluorescence, scanning electron microscopy and Brunauer–Emmett–Teller techniques. Copyright © 2005 Society of Chemical Industry     

Artificial neural network,ANN-PSO and ANN-ICA for modelling the Stirling engine

Nowadays, shortage of fossil fuels resources, especially oil, and also global attention to environmental hazards produced by the internal combustion process have caused extensive researches on the development of renewable energy engine technology. Among all kinds of renewable resources, solar energy Stirling engines have their own special situation for energy generation with lower pollutants and sustainable sources. The Stirling engine is an external combustion engine that uses any external heat source to generate mechanical power. Various parameters affect the performance of the Stirling engine. In this study, artificial neural network (ANN) was applied to estimate the power and torque values obtained from a Stirling heat engine (Philips M102C engine). It employs the Levenberg–Marquardt algorithm for training ANN with back propagation network for estimating the power and torque of the Stirling heat engine. The performances of the imperialist competitive algorithm (ICA)-ANN and ANN-particle swarm optimisation (PSO) are compared with the performance of the ANN based on mean square error (MSE) and correlation coefficient. PSO and ICAs are applied to determine the initial weights of the neural network. The obtained results indicate that ANN-PSO has a better performance than ICA-ANN and ANN alone; also the MSE for the ANN-PSO is lower as well. Considering the results obtained from this study, there is very good agreement between the output of the testing phase of the ANN-PSO model with experimental data and they are very close to each other.     

Nondominated Archiving Multicolony Ant Algorithm in Time–Cost Trade-Off Optimization

Time–cost trade-off analysis is addressed as an important aspect of any construction project planning and control. Nonexistence of a unique solution makes the time–cost trade-off problems very difficult to tackle. As a combinatorial optimization problem one may apply heuristics or mathematical programming techniques to solve time–cost trade-off problems. In this paper, a new multicolony ant algorithm is developed and used to solve the time–cost multiobjective optimization problem. Pareto archiving together with innovative solution exchange strategy are introduced which are highly efficient in developing the Pareto front and set of nondominated solutions in a time–cost optimization problem. An 18-activity time–cost problem is used to evaluate the performance of the proposed algorithm. Results show that the proposed algorithm outperforms the well-known weighted method to develop the nondominated solutions in a combinatorial optimization problem. The paper is more relevant to researchers who are interested in developing new quantitative methods and/or algorithms for managing construction projects.     

Multi-objective optimization in a finite time thermodynamic method for dish-Stirling by branch and bound method and MOPSO algorithm

There are various analyses for a solar system with the dish-Stirling technology. One of those analyses is the finite time thermodynamic analysis by which the total power of the system can be obtained by calculating the process time. In this study, the convection and radiation heat transfer losses from collector surface, the conduction heat transfer between hot and cold cylinders, and cold side heat exchanger have been considered. During this investigation, four objective functions have been optimized simultaneously, including power, efficiency, entropy, and economic factors. In addition to the four-objective optimization, three-objective, two-objective, and single-objective optimizations have been done on the dish-Stirling model. The algorithm of multi-objective particle swarm optimization (MOPSO) with post-expression of preferences is used for multi-objective optimizations while the branch and bound algorithm with pre-expression of preferences is used for single-objective and multi-objective optimizations. In the case of multi-objective optimizations with post-expression of preferences, Pareto optimal front are obtained, afterward by implementing the fuzzy, LINMAP, and TOPSIS decision making algorithms, the single optimum results can be achieved. The comparison of the results shows the benefits of MOPSO in optimizing dish Stirling finite time thermodynamic equations.